Article in Science translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
29 authors.
Alan L ChangDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0003-1716-0134
Jonathan D ReissDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0003-1460-8570
Anthony CulosDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
Martin BeckerDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0003-4296-3481
Jonathan A MayoDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0003-0361-4078
Ivana MarićDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0002-9441-521X
Davide De FrancescoDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
Thanaphong PhongpreechaDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0001-9245-6686
Camilo A EspinosaDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0003-1630-1564
Samson J MatarasoDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0003-3146-2243
Eloïse BersonDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0003-1046-125X
Yeasul KimDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0001-8289-1297
Lei XueDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0002-4236-8714
Feng XieDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0002-0215-667X
Chi-Hung ShuDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0009-0009-3486-8856
Ramin FallahzadehDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
Neda H BidokiDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0002-6741-6735
Maria XenochristouDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
Miao ZhangDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
Jochen ProfitDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Henry C LeeDepartment of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA.ORCID 0000-0001-8383-1720
Brice GaudillièreDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0002-3475-5706
Martin S AngstDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0002-1550-8136
Steven HawkenDepartment of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada.ORCID 0000-0002-3341-9022
Kumanan WilsonDepartment of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada.ORCID 0000-0002-1741-7705
David K StevensonDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Gary M ShawDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0001-7438-4914
Karl G SylvesterDepartment of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0002-8559-0155
Nima AghaeepourDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.ORCID 0000-0002-6117-8764
Funding
Machine Learning for Integrative Modeling of the Immune System in Clinical SettingsR35GM138353 · NIGMS · STANFORD UNIVERSITY · PI AGHAEEPOUR, NIMA · 2020 to 2024
$2.2M
An Investigation of Omics for Bronchopulmonary DypslasiaK23HL173645 · NHLBI · STANFORD UNIVERSITY · PI JONATHAN REISS · 2025 to 2026
Neonatal prematurity leads to considerable morbidity and mortality, partly because of acquired conditions such as bronchopulmonary dysplasia (BPD), intraventricular hemorrhage (IVH), necrotizing enterocolitis (NEC), and retinopathy of prematurity (ROP). Standard gestational age and birthweight-based classifications of prematurity inadequately capture the variation in newborns' health outcomes, creating an urgent need to develop risk stratification tools for vulnerable newborn infants to initiate the most appropriate care pathways as early as possible. We hypothesized that the metabolic profiles of newborn infants capture additional risk information beyond current measures. A total of 13,536 newborn screening (NBS) blood spot tests from preterm infants in California with linked clinical outcomes of prematurity were used to develop an NBS-based metabolic health index to stratify preterm infants at risk for BPD, IVH, NEC, and ROP (12,096 cases with one or more conditions and 1440 controls) through a deep learning model that provides a single index score in tandem with subgroup discovery to identify individuals with the strongest metabolite biomarker signals for adverse outcomes of prematurity. This metabolic health index captured risk signals that were distinct from gestational age and birthweight and outperformed other machine learning algorithms and clinical risk variable-based models in stratifying at-risk individuals for adverse outcomes of prematurity. The metabolic health index was externally validated in an independent retrospective cohort of 3299 very premature newborns from Ontario, Canada (2117 cases and 1182 controls), which recapitulated common metabolic risk subgroups. In summary, combining widespread metabolite screening with deep learning established a generalizable biological risk metric of prematurity.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Quantitative assessment of neonatal health using dried blood spot metabolite profiles and deep learning. · full record | OpenQuestion